HackerEarth Blog

7 Powerful Programming Languages For Doing Machine Learning http://blog.hackerearth.com/powerful-programming-languages-for-machine… Python if a popular scientific language and a rising star for machine learning. I'd be surprised if it can take the data analysis mantle from R, but matrix handling in NumPy may challenge MATLAB and communication tools like IPython are very attractive and a step into the future of reproducibility.

Tl:dr- In order; graph theory, linear algebra, number theory, and multi-criteria decision analysis. Game theory, set theory, nonlinearity, computational geometry a bonus. Let's say you're learning some new algorithms. Your list might look something like the below list[1]. I'll add in brackets the sort of math you learn about by learning the algo if you get lost down the wiki-rabbit-hole. If you want to solve other similar problems, more of that sort of math will help. 1) Graph algorithms [graph theory]: Breadth first search(BFS), Depth first search(DFS), Strongly connected components(SCC), Dijkstra, Floyd-Warshall, Minimum spanning tree(MST), Topological sort. 2) Dynamic programming [self-similarity, finite subdivision rules, nonlinear equations]: Standard dynamic programming problems such as Rod Cutting, Knapsack, Matrix chain multiplication etc. 3) Number theory [Number Theory]: Modular arithmetic, Fermat’s theorem, Chinese remainder theorem(CRT), Euclidian method for GCD, Logarithmic Exponentiation, Sieve of Eratosthenes, Euler’s totient function. 3) Greedy [goal programming, simplex algorithm, multi-criteria decision analysis]: Standard problems such as Activity selection. 4) Search techniques [linear/vector algebra[2]]: Binary search, Ternary search and Meet in the middle. 5) Data structures (Basic) [graph theory]: Stacks, Queues, Trees and Heaps. 6) Data structures (Advanced)[more graph theory]: Trie, Segment trees, Fenwick tree or Binary indexed tree(BIT),…